Run to run process control has been widely developed and utilized in different manufacturing industries where the processing conditions of next run are adjusted based on prior run results. A simple exponentially weighted moving average (EWMA) tuner is often adopted in the run to run process control system to estimate the deviation of model parameters. The simple EWMA tuner can be expressed using the formula:Pn+1=λ×Rn+(1−λ)×Pn 
where:                P=Process Control Parameter        R=Predicted Process Model Parameter based on Actual Data (e.g., new data point)        λ=EWMA weighing factor (0<λ<1)        n=nth run        
The EWMA weighing factor, lambda, is usually carefully selected for a process in order to get adequate process tuning. One such approach involves determining an optimum/fixed lambda value based on process capability index values and is described in U.S. Pat. No. 7,809,459 to Morisawa et. al. The simple EWMA tuner is usually quite effective to bring a process with moderate drifts under control, but may not be able to tune the process quick enough to address severe process shifts. Using a large fixed lambda value with the EWMA tuner as might be used in the Morisawa system could accelerate process tuning, but it does not allow for adjustment of the lambda value on the fly and thus introduces a high risk of process over-tuning.
A predictor corrector control scheme, which can also be referred to as a double EWMA controller, is proposed in an article by S. Butler and J. Stefani entitled, “Supervisory Run-to-Run Control of Polysilicon Gate Etch Using In Situ Ellipsometry,” IEEE Trans. Semiconduct. Manufact., vol. 7, no. 2, pp. 193-201, 1994. In this predictor control scheme, a second EWMA tuner is used to compensate for the error incurred from the simple EWMA tuner, so extra tuning can be applied to the model control parameter when large process drifts occur. However, while this approach may be capable of adapting to small changes in process output, it is not well suited to adapting to high magnitude or short time interval type changes in process output.
Another approach that deals with both process drifts and process shifts at the same time has been proposed by R. Guo, A. Chen, and J. Chen, in a portion of a book entitled, “Chapter 19 An Enhanced EWMA Controller for Processes Subject to Random Disturbances,” Run-to-Run Control in Semiconductor Manufacturing, edited by J. Moyne, E. del Castillo, and A. M. Hurwitz, CRC Press LLC, 2001. In the enhanced EWMA controller proposed by Guo, a baseline lambda is utilized to compensate for smaller process drifts. Two EWMA control charts are used as the detection tools for large and medium process shifts. If there is an out-of-control signal on the charts, a dynamic tuning loop is triggered and the lambda value is reset to a higher value. The lambda value is then decreased gradually over the next few runs, and eventually back to the baseline lambda. However, while this approach may be capable of some dynamic tuning using the control charts to tune to particular shifts, it is not well suited for rapid fluctuations in process output as the approach does not tune particularly fast.
Accordingly, an improved run to run control system that can address these shortcomings is needed.